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Case-control comparison brain lesion segmentation for early infarct detection.

Fung Fung Ting1, Kok Swee Sim1, Chee Peng Lim2

  • 1Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, 75450, Melaka, Malaysia.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|September 17, 2018
PubMed
Summary

This study introduces a new method for automatically detecting brain infarcts in Computed Tomography (CT) images. The case-control comparison brain lesion segmentation (CCBLS) method aids doctors in stroke diagnosis by segmenting brain lesions efficiently.

Keywords:
Biomedical engineeringBrain lesionComputerized support of stroke diagnosisMedical imaging processingStroke

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Area of Science:

  • Medical Imaging
  • Radiology
  • Neurology

Background:

  • Computed Tomography (CT) imaging is crucial for identifying brain abnormalities after stroke.
  • Manual lesion delineation by physicians is the current standard but is time-consuming and subjective.
  • Accurate and efficient brain lesion segmentation is vital for stroke diagnosis and treatment.

Purpose of the Study:

  • To develop an automated method for segmenting brain lesions in CT images.
  • To reduce the burden on medical professionals in stroke diagnosis.
  • To improve the accuracy and efficiency of identifying brain infarcts and hemorrhages.

Main Methods:

  • A novel case-control comparison brain lesion segmentation (CCBLS) method was proposed.
  • The CCBLS method compares voxel intensity between control subjects and stroke patients.
  • Automated segmentation of brain lesions from stacked CT images without prior location knowledge.

Main Results:

  • The CCBLS method demonstrated effectiveness in segmenting brain lesions.
  • The study utilized a dataset of 300 CT image sets from control and stroke patients.
  • The proposed method showed superior performance in detecting brain infarcts compared to existing techniques.

Conclusions:

  • The CCBLS method offers an effective and automated solution for brain lesion segmentation.
  • This approach can assist physicians in making accurate and timely stroke diagnoses.
  • The findings highlight the potential of automated image analysis in neuroimaging for stroke care.